U.S. Pat. No. 6,185,470 (the '470 patent) addressed a solution to a long-standing need. The '470 patent recognized that many dynamic, nonlinear systems exist which need adaptive forms of control. Just some of the problems arising were vibration and undesirable aeroelastic responses adversely affecting various flexible structures such as an aircraft wing. These adverse effects shortened the life spans and increased the acquisition and maintenance costs of such structures. The active control system presented by the '470 patent is useful for reducing vibration, alleviating buffet load and suppressing flutter of aircraft structures, providing adaptive hydraulic load control, reducing limit cycle oscillations of an aircraft store, and providing other solutions.
The nonlinear adaptive controller provided by the '470 patent is not system specific and learns nonlinearities in a neural network. Further, the controller has a relatively fast time constant of about one millisecond or faster and does not need to copy the actions of another controller which must first be developed. The nonlinear adaptive controller provided by the '470 patent provides these benefits through the use of a neural network adaptive controller which provides improved control performance over that of a conventional fixed gain controller.
FIG. 1A is a block diagram of a system 100 using such a control system 110. The control system 110 receives a control input 120. A control system output 130 is applied as a plant input to an operational plant 140 which in turn yields a plant output 150. An object of the control system 110 to control and stabilize the plant output 150 by applying an appropriate control output/plant input 130. An effective control output 130 is one that is both stable and has high gain.
More specifically, the neural network adaptive controller of the '470 patent uses online learning neural networks to implement an adaptive, self-optimizing controller for an operational plant. As shown in FIG. 1B, the method of the '470 patent creates a neural network model 160 that stores past system states 170 in response to past control inputs 180. Based on future control inputs 190, the neural network model yields predicted future states 195 of the operational plant (not shown).
As shown in FIG. 2, the '470 patent create a neural network controller 200 for controlling an operational plant 210. The neural network controller 200 includes a performance optimization index or cost function 220 which is a function of the future output states 140 of the neural network model 100. In the system using the neural network controller 200, a reference model 240 generates an output 230 which is received by the cost function 220 along with the future output states 140. A control output 250 of the cost function 220 helps to provide stable, effective control of the operational plant 210.
However, tuning the cost function can be a time-consuming and labor-intensive process. Tuning the cost function can require detailed and repetitive manipulations of the cost function parameters to achieve stable and effective control. Moreover, attempting to avoid these calculations by simply experimenting with various cost function parameters to test those parameters possibly can result in damage to the operational plant if the parameters do not yield stable controllers.
Thus, there is an unmet need in the art for an efficient and safe method by which to tune cost function parameters to effectively control an operational plant with a neural network adaptive controller.